Planning with dynamic state a trajectory of an autonomous vehicle

ABSTRACT

This disclosure describes an autonomous vehicle configured to obtain sensor data associated with objects proximate a projected route of the autonomous vehicle, determine static constraints that limit a trajectory of the autonomous vehicle along the projected route based on non-temporal risks associated with a first subset of the f objects, predict a position and speed of the autonomous vehicle as a function of time along the projected route based on the static constraints, identify temporal risks associated with a second subset of the objects based on the predicted position and speed of the autonomous vehicle, determine dynamic constraints that further limit the trajectory of the autonomous vehicle along the projected route to help the autonomous vehicle avoid the temporal risks associated with the second subset of the objects, and adjust the trajectory of the autonomous vehicle in accordance with the static constraints and the dynamic constraints.

FIELD

This description relates to a computer system for detecting one or moreobjects in the environment surrounding an autonomous vehicle andpredicting a behavior of the one or more objects using machine learningtechniques.

BACKGROUND

Autonomous vehicles can be used to transport people and/or cargo (e.g.,packages, objects, or other items) from one location to another. Forexample, an autonomous vehicle can navigate to the location of a person,wait for the person to board the autonomous vehicle, and navigate to aspecified destination (e.g., a location selected by the person). Tonavigate in the environment, these autonomous vehicles are equipped withvarious types of sensors to detect objects in the surroundings.

SUMMARY

Anticipating the behavior of objects being tracked by the sensors can bedifficult. The subject matter described in this specification isdirected to a computer system and techniques for detecting andpredicting the behavior of objects in an environment surrounding anautonomous vehicle. Generally, the computer system is configured toreceive input from one or more sensors of the vehicle, detect one ormore objects in the environment surrounding the vehicle based on thereceived input, and operate the vehicle based upon the predictedbehavior of the objects.

In particular, a system is disclosed that includes at least oneprocessor and at least one memory storing instructions thereon that,when executed by the at least one processor, cause the at least oneprocessor to perform operations comprising: obtaining sensor datagenerated by a sensor of an autonomous vehicle, the sensor data beingassociated with a plurality of objects proximate a projected route ofthe autonomous vehicle; determining static constraints that limit atrajectory of the autonomous vehicle along the projected route based onnon-temporal risks associated with a first subset of the plurality ofobjects; predicting a position and speed of the autonomous vehicle as afunction of time along the projected route based on the staticconstraints; identifying temporal risks associated with a second subsetof the plurality of objects based on the predicted position and speed ofthe autonomous vehicle; determining dynamic constraints that furtherlimit the trajectory of the autonomous vehicle along the projected routeto help the autonomous vehicle avoid the temporal risks associated withthe second subset of the plurality of objects; and adjusting thetrajectory of the autonomous vehicle in accordance with the staticconstraints and the dynamic constraints; and navigating the autonomousvehicle in accordance with the adjusted trajectory.

A non-transitory computer-readable storage medium is disclosed thatincludes instructions stored thereon that, when executed by at least oneprocessor, cause the at least one processor to carry out operationscomprising: obtaining sensor data generated by a sensor of an autonomousvehicle, the sensor data being associated with a plurality of objectsproximate a projected route of the autonomous vehicle; determiningstatic constraints that limit a trajectory of the autonomous vehiclealong the projected route based on non-temporal risks associated with afirst subset of the plurality of objects; predicting a position andspeed of the autonomous vehicle as a function of time along theprojected route based on the static constraints; identifying temporalrisks associated with a second subset of the plurality of objects basedon the predicted position and speed of the autonomous vehicle;determining dynamic constraints that further limit the trajectory of theautonomous vehicle along the projected route to help the autonomousvehicle avoid the temporal risks associated with the second subset ofthe plurality of objects; adjusting the trajectory of the autonomousvehicle in accordance with the static constraints and the dynamicconstraints; and navigating the autonomous vehicle in accordance withthe adjusted trajectory.

A method performed by an autonomous vehicle following a projected routeis disclosed. The method includes: obtaining sensor data generated by asensor of an autonomous vehicle, the sensor data being associated with aplurality of objects proximate a projected route of the autonomousvehicle; determining static constraints that limit a trajectory of theautonomous vehicle along the projected route based on non-temporal risksassociated with a first subset of the plurality of objects; predicting aposition and speed of the autonomous vehicle as a function of time alongthe projected route based on the static constraints; identifyingtemporal risks associated with a second subset of the plurality ofobjects based on the predicted position and speed of the autonomousvehicle; determining dynamic constraints that further limit thetrajectory of the autonomous vehicle along the projected route to helpthe autonomous vehicle avoid the temporal risks associated with thesecond subset of the plurality of objects; adjusting the trajectory ofthe autonomous vehicle in accordance with the static constraints and thedynamic constraints; and navigating the autonomous vehicle in accordancewith the adjusted trajectory.

These and other aspects, features, and implementations can be expressedas methods, apparatuses, systems, components, program products, means orsteps for performing a function, and in other ways.

These and other aspects, features, and implementations will becomeapparent from the following descriptions, including the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an autonomous vehicle having autonomouscapability.

FIG. 2 illustrates an example “cloud” computing environment.

FIG. 3 illustrates a computer system.

FIG. 4 shows an example architecture for an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that may be used by aperception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows the operation of the LiDAR system in additional detail.

FIG. 9 shows a block diagram of the relationships between inputs andoutputs of a planning module.

FIG. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of a controlmodule.

FIG. 12 shows a block diagram of the inputs, outputs, and components ofa controller.

FIGS. 13A-13B show graphs of velocity squared versus positionillustrating how soft and hard constraints affect a projected route andmaximum acceleration/deceleration profiles associated with an autonomousvehicle in accordance with some embodiments.

FIG. 14A shows a top down view of an autonomous vehicle traversing astreet as a traffic light at a traffic light junction turns yellow.

FIG. 14B shows a graph of velocity squared versus position illustratinghow a construction zone proximate the traffic light junction depicted inFIG. 14A affects the ability of the autonomous vehicle to get throughthe traffic light junction before it turns red in accordance with someembodiments.

FIGS. 15A-15C show a scenario in which an autonomous vehicle utilizesthe described embodiments to respond to a pedestrian crossing thestreet.

FIG. 16A shows a scenario in which an autonomous vehicle is approachingan intersection and determines, based on the behavior of anothervehicle, that the other vehicle is likely to enter the intersection atthe same time as the autonomous vehicle.

FIG. 16B shows a graph of velocity squared versus position illustratinghow a minimum speed profile can be applied to get the autonomous vehicledepicted in FIG. 16A through the intersection safely in accordance withsome embodiments.

FIG. 17 is a flow chart of an example process 1700 for detecting objectsin an environment and operating the vehicle based on the detection ofobjects.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that the present disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

In the drawings, specific arrangements or orderings of schematicelements, such as those representing devices, modules, instructionblocks and data elements, are shown for ease of description. However, itshould be understood by those skilled in the art that the specificordering or arrangement of the schematic elements in the drawings is notmeant to imply that a particular order or sequence of processing, orseparation of processes, is required. Further, the inclusion of aschematic element in a drawing is not meant to imply that such elementis required in all embodiments or that the features represented by suchelement may not be included in or combined with other elements in someembodiments.

Further, in the drawings, where connecting elements, such as solid ordashed lines or arrows, are used to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not shown in the drawings so as not to obscure thedisclosure. In addition, for ease of illustration, a single connectingelement is used to represent multiple connections, relationships orassociations between elements. For example, where a connecting elementrepresents a communication of signals, data, or instructions, it shouldbe understood by those skilled in the art that such element representsone or multiple signal paths (e.g., a bus), as may be needed, to affectthe communication.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Several features are described hereafter that can each be usedindependently of one another or with any combination of other features.However, any individual feature may not address any of the problemsdiscussed above or might only address one of the problems discussedabove. Some of the problems discussed above might not be fully addressedby any of the features described herein. Although headings are provided,information related to a particular heading, but not found in thesection having that heading, may also be found elsewhere in thisdescription. Embodiments are described herein according to the followingoutline:

-   -   1. General Overview    -   2. Hardware Overview    -   3. Autonomous Vehicle Architecture    -   4. Autonomous Vehicle Inputs    -   5. Autonomous Vehicle Planning    -   6. Autonomous Vehicle Control    -   7. Computing System for Object Detection Using Pillars    -   8. Example Point Clouds and Pillars    -   9. Example Process for Detecting Objects and Operating the        Vehicle Based on the Detection of the Objects

General Overview

Autonomous vehicles driving in complex environments (e.g., an urbanenvironment) pose a great technological challenge. In order forautonomous vehicles to navigate these environments, the vehicles detectvarious types of objects such as vehicles, pedestrians, and bikes inreal-time using sensors such as LIDAR, optical imagery and/or RADAR.While these sensors are able to identify and track objects, predictingthe behavior of the objects can be challenging and treating the trackedobjects too conservatively can result in autonomous vehicles beingunable to function. The disclosed embodiments include a system andtechniques for predicting the behavior of and efficiently avoidingdetected objects.

In particular, the system and techniques described herein implementmachine learning (e.g., a neural network) that can correlate sensor datafrom detected objects with sensor data from previously tracked objects.The autonomous vehicle is then able to use the behavior of thepreviously tracked objects to predict the movement of detected objectsproximate a projected route of travel of the autonomous vehicle. Thepredicted movement can then be used to identify a temporal zone likelyto contain the detected object. When the predicted movement is ofsufficient quality, the autonomous vehicle can adjust speed and/orposition to efficiently avoid any unintended collisions with one or moredynamic objects.

Hardware Overview

FIG. 1 shows an example of an autonomous vehicle 100 having autonomouscapability.

As used herein, the term “autonomous capability” refers to a function,feature, or facility that enables a vehicle to be partially or fullyoperated without real-time human intervention, including withoutlimitation fully autonomous vehicles, highly autonomous vehicles, andconditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possessesautonomous capability.

As used herein, “vehicle” includes means of transportation of goods orpeople. For example, cars, buses, trains, airplanes, drones, trucks,boats, ships, submersibles, dirigibles, etc. A driverless car is anexample of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AVfrom a first spatiotemporal location to a second spatiotemporallocation. In an embodiment, the first spatiotemporal location isreferred to as the initial or starting location and the secondspatiotemporal location is referred to as the destination, finallocation, goal, goal position, or goal location. In some examples, atrajectory is made up of one or more segments (e.g., sections of road)and each segment is made up of one or more blocks (e.g., portions of alane or intersection). In an embodiment, the spatiotemporal locationscorrespond to real world locations. For example, the spatiotemporallocations are pick up or drop-off locations to pick up or drop-offpersons or goods.

As used herein, “sensor(s)” includes one or more hardware componentsthat detect information about the environment surrounding the sensor.Some of the hardware components can include sensing components (e.g.,image sensors, biometric sensors), transmitting and/or receivingcomponents (e.g., laser or radio frequency wave transmitters andreceivers), electronic components such as analog-to-digital converters,a data storage device (such as a RAM and/or a nonvolatile storage),software or firmware components and data processing components such asan ASIC (application-specific integrated circuit), a microprocessorand/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list)or data stream that includes one or more classified or labeled objectsdetected by one or more sensors on the AV vehicle or provided by asource external to the AV.

As used herein, a “road” is a physical area that can be traversed by avehicle, and may correspond to a named thoroughfare (e.g., city street,interstate freeway, etc.) or may correspond to an unnamed thoroughfare(e.g., a driveway in a house or office building, a section of a parkinglot, a section of a vacant lot, a dirt path in a rural area, etc.).Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utilityvehicles, etc.) are capable of traversing a variety of physical areasnot specifically adapted for vehicle travel, a “road” may be a physicalarea not formally defined as a thoroughfare by any municipality or othergovernmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed bya vehicle, and may correspond to most or all of the space between lanemarkings, or may correspond to only some (e.g., less than 50%) of thespace between lane markings. For example, a road having lane markingsspaced far apart might accommodate two or more vehicles between themarkings, such that one vehicle can pass the other without traversingthe lane markings, and thus could be interpreted as having a lanenarrower than the space between the lane markings, or having two lanesbetween the lane markings. A lane could also be interpreted in theabsence of lane markings. For example, a lane may be defined based onphysical features of an environment, e.g., rocks and trees along athoroughfare in a rural area.

“One or more” includes a function being performed by one element, afunction being performed by more than one element, e.g., in adistributed fashion, several functions being performed by one element,several functions being performed by several elements, or anycombination of the above.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first contactcould be termed a second contact, and, similarly, a second contact couldbe termed a first contact, without departing from the scope of thevarious described embodiments. The first contact and the second contactare both contacts, but they are not the same contact, unless specifiedotherwise.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this description, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

As used herein, an AV system refers to the AV along with the array ofhardware, software, stored data, and data generated in real-time thatsupports the operation of the AV. In an embodiment, the AV system isincorporated within the AV. In an embodiment, the AV system is spreadacross several locations. For example, some of the software of the AVsystem is implemented on a cloud computing environment similar to cloudcomputing environment 200 described below with respect to FIG. 2.

In general, this document describes technologies applicable to anyvehicles that have one or more autonomous capabilities including fullyautonomous vehicles, highly autonomous vehicles, and conditionallyautonomous vehicles, such as so-called Level 5, Level 4 and Level 3vehicles, respectively (see SAE International's standard J3016: Taxonomyand Definitions for Terms Related to On-Road Motor Vehicle AutomatedDriving Systems, which is incorporated by reference in its entirety, formore details on the classification of levels of autonomy in vehicles).The technologies described in this document are also applicable topartially autonomous vehicles and driver assisted vehicles, such asso-called Level 2 and Level 1 vehicles (see SAE International's standardJ3016: Taxonomy and Definitions for Terms Related to On-Road MotorVehicle Automated Driving Systems). In an embodiment, one or more of theLevel 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicleoperations (e.g., steering, braking, and using maps) under certainoperating conditions based on processing of sensor inputs. Thetechnologies described in this document can benefit vehicles in anylevels, ranging from fully autonomous vehicles to human-operatedvehicles.

Referring to FIG. 1, an AV system 120 operates the AV 100 along atrajectory 198 through an environment 190 to a destination 199(sometimes referred to as a final location) while avoiding objects(e.g., natural obstructions 191, vehicles 193, pedestrians 192,cyclists, and other obstacles) and obeying rules of the road (e.g.,rules of operation or driving preferences).

In an embodiment, the AV system 120 includes devices 101 that areinstrumented to receive and act on operational commands from thecomputer processors 146. In an embodiment, computing processors 146 aresimilar to the processor 304 described below in reference to FIG. 3.Examples of devices 101 include a steering control 102, brakes 103,gears, accelerator pedal or other acceleration control mechanisms,windshield wipers, side-door locks, window controls, andturn-indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuringor inferring properties of state or condition of the AV 100, such as theAV's position, linear and angular velocity and acceleration, and heading(e.g., an orientation of the leading end of AV 100). Example of sensors121 are GPS, inertial measurement units (IMU) that measure both vehiclelinear accelerations and angular rates, wheel speed sensors formeasuring or estimating wheel slip ratios, wheel brake pressure orbraking torque sensors, engine torque or wheel torque sensors, andsteering angle and angular rate sensors.

In an embodiment, the sensors 121 also include sensors for sensing ormeasuring properties of the AV's environment. For example, monocular orstereo video cameras 122 in the visible light, infrared or thermal (orboth) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight(TOF) depth sensors, speed sensors, temperature sensors, humiditysensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 andmemory 144 for storing machine instructions associated with computerprocessors 146 or data collected by sensors 121. In an embodiment, thedata storage unit 142 is similar to the ROM 308 or storage device 310described below in relation to FIG. 3. In an embodiment, memory 144 issimilar to the main memory 306 described below. In an embodiment, thedata storage unit 142 and memory 144 store historical, real-time, and/orpredictive information about the environment 190. In an embodiment, thestored information includes maps, driving performance, trafficcongestion updates or weather conditions. In an embodiment, datarelating to the environment 190 is transmitted to the AV 100 via acommunications channel from a remotely located database 134.

In an embodiment, the AV system 120 includes communications devices 140for communicating measured or inferred properties of other vehicles'states and conditions, such as positions, linear and angular velocities,linear and angular accelerations, and linear and angular headings to theAV 100. These devices include Vehicle-to-Vehicle (V2V) andVehicle-to-Infrastructure (V2I) communication devices and devices forwireless communications over point-to-point or ad hoc networks or both.In an embodiment, the communications devices 140 communicate across theelectromagnetic spectrum (including radio and optical communications) orother media (e.g., air and acoustic media). A combination ofVehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication(and, in some embodiments, one or more other types of communication) issometimes referred to as Vehicle-to-Everything (V2X) communication. V2Xcommunication typically conforms to one or more communications standardsfor communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices 140 include communicationinterfaces. For example, wired, wireless, WiMAX, WiFi, Bluetooth,satellite, cellular, optical, near field, infrared, or radio interfaces.The communication interfaces transmit data from a remotely locateddatabase 134 to AV system 120. In an embodiment, the remotely locateddatabase 134 is embedded in a cloud computing environment 200 asdescribed in FIG. 2. The communication interfaces 140 transmit datacollected from sensors 121 or other data related to the operation of AV100 to the remotely located database 134. In an embodiment,communication interfaces 140 transmit information that relates toteleoperations to the AV 100. In some embodiments, the AV 100communicates with other remote (e.g., “cloud”) servers 136.

In an embodiment, the remotely located database 134 also stores andtransmits digital data (e.g., storing data such as road and streetlocations). Such data is stored on the memory 144 on the AV 100, ortransmitted to the AV 100 via a communications channel from the remotelylocated database 134.

In an embodiment, the remotely located database 134 stores and transmitshistorical information about driving properties (e.g., speed andacceleration profiles) of vehicles that have previously traveled alongtrajectory 198 at similar times of day. In one implementation, such datamay be stored on the memory 144 on the AV 100, or transmitted to the AV100 via a communications channel from the remotely located database 134.

Computing devices 146 located on the AV 100 algorithmically generatecontrol actions based on both real-time sensor data and priorinformation, allowing the AV system 120 to execute its autonomousdriving capabilities.

In an embodiment, the AV system 120 includes computer peripherals 132coupled to computing devices 146 for providing information and alertsto, and receiving input from, a user (e.g., an occupant or a remoteuser) of the AV 100. In an embodiment, peripherals 132 are similar tothe display 312, input device 314, and cursor controller 316 discussedbelow in reference to FIG. 3. The coupling is wireless or wired. Any twoor more of the interface devices may be integrated into a single device.

FIG. 2 illustrates an example “cloud” computing environment. Cloudcomputing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services). Intypical cloud computing systems, one or more large cloud data centershouse the machines used to deliver the services provided by the cloud.Referring now to FIG. 2, the cloud computing environment 200 includescloud data centers 204 a, 204 b, and 204 c that are interconnectedthrough the cloud 202. Data centers 204 a, 204 b, and 204 c providecloud computing services to computer systems 206 a, 206 b, 206 c, 206 d,206 e, and 206 f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud datacenters. In general, a cloud data center, for example the cloud datacenter 204 a shown in FIG. 2, refers to the physical arrangement ofservers that make up a cloud, for example the cloud 202 shown in FIG. 2,or a particular portion of a cloud. For example, servers are physicallyarranged in the cloud datacenter into rooms, groups, rows, and racks. Acloud datacenter has one or more zones, which include one or more roomsof servers. Each room has one or more rows of servers, and each rowincludes one or more racks. Each rack includes one or more individualserver nodes. In some implementation, servers in zones, rooms, racks,and/or rows are arranged into groups based on physical infrastructurerequirements of the datacenter facility, which include power, energy,thermal, heat, and/or other requirements. In an embodiment, the servernodes are similar to the computer system described in FIG. 3. The datacenter 204 a has many computing systems distributed through many racks.

The cloud 202 includes cloud data centers 204 a, 204 b, and 204 c alongwith the network and networking resources (for example, networkingequipment, nodes, routers, switches, and networking cables) thatinterconnect the cloud data centers 204 a, 204 b, and 204 c and helpfacilitate the computing systems' 206 a-f access to cloud computingservices. In an embodiment, the network represents any combination ofone or more local networks, wide area networks, or internetworks coupledusing wired or wireless links deployed using terrestrial or satelliteconnections. Data exchanged over the network, is transferred using anynumber of network layer protocols, such as Internet Protocol (IP),Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM),Frame Relay, etc. Furthermore, in embodiments where the networkrepresents a combination of multiple sub-networks, different networklayer protocols are used at each of the underlying sub-networks. In someembodiments, the network represents one or more interconnectedinternetworks, such as the public Internet.

The computing systems 206 a-f or cloud computing services consumers areconnected to the cloud 202 through network links and network adapters.In an embodiment, the computing systems 206 a-f are implemented asvarious computing devices, for example servers, desktops, laptops,tablet, smartphones, Internet of Things (IoT) devices, autonomousvehicles (including, cars, drones, shuttles, trains, buses, etc.) andconsumer electronics. In an embodiment, the computing systems 206 a-fare implemented in or as a part of other systems.

FIG. 3 illustrates a computer system 300. In an implementation, thecomputer system 300 is a special purpose computing device. Thespecial-purpose computing device is hard-wired to perform the techniquesor includes digital electronic devices such as one or moreapplication-specific integrated circuits (ASICs) or field programmablegate arrays (FPGAs) that are persistently programmed to perform thetechniques, or may include one or more general purpose hardwareprocessors programmed to perform the techniques pursuant to programinstructions in firmware, memory, other storage, or a combination. Suchspecial-purpose computing devices may also combine custom hard-wiredlogic, ASICs, or FPGAs with custom programming to accomplish thetechniques. In various embodiments, the special-purpose computingdevices are desktop computer systems, portable computer systems,handheld devices, network devices or any other device that incorporateshard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system 300 includes a bus 302 or othercommunication mechanism for communicating information, and a hardwareprocessor 304 coupled with a bus 302 for processing information. Thehardware processor 304 is, for example, a general-purposemicroprocessor. The computer system 300 also includes a main memory 306,such as a random-access memory (RAM) or other dynamic storage device,coupled to the bus 302 for storing information and instructions to beexecuted by processor 304. In one implementation, the main memory 306 isused for storing temporary variables or other intermediate informationduring execution of instructions to be executed by the processor 304.Such instructions, when stored in non-transitory storage mediaaccessible to the processor 304, render the computer system 300 into aspecial-purpose machine that is customized to perform the operationsspecified in the instructions.

In an embodiment, the computer system 300 further includes a read onlymemory (ROM) 308 or other static storage device coupled to the bus 302for storing static information and instructions for the processor 304. Astorage device 310, such as a magnetic disk, optical disk, solid-statedrive, or three-dimensional cross point memory is provided and coupledto the bus 302 for storing information and instructions.

In an embodiment, the computer system 300 is coupled via the bus 302 toa display 312, such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), plasma display, light emitting diode (LED) display, or anorganic light emitting diode (OLED) display for displaying informationto a computer user. An input device 314, including alphanumeric andother keys, is coupled to bus 302 for communicating information andcommand selections to the processor 304. Another type of user inputdevice is a cursor controller 316, such as a mouse, a trackball, atouch-enabled display, or cursor direction keys for communicatingdirection information and command selections to the processor 304 andfor controlling cursor movement on the display 312. This input devicetypically has two degrees of freedom in two axes, a first axis (e.g.,x-axis) and a second axis (e.g., y-axis), that allows the device tospecify positions in a plane.

According to one embodiment, the techniques herein are performed by thecomputer system 300 in response to the processor 304 executing one ormore sequences of one or more instructions contained in the main memory306. Such instructions are read into the main memory 306 from anotherstorage medium, such as the storage device 310. Execution of thesequences of instructions contained in the main memory 306 causes theprocessor 304 to perform the process steps described herein. Inalternative embodiments, hard-wired circuitry is used in place of or incombination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media includes non-volatilemedia and/or volatile media. Non-volatile media includes, for example,optical disks, magnetic disks, solid-state drives, or three-dimensionalcross point memory, such as the storage device 310. Volatile mediaincludes dynamic memory, such as the main memory 306. Common forms ofstorage media include, for example, a floppy disk, a flexible disk, harddisk, solid-state drive, magnetic tape, or any other magnetic datastorage medium, a CD-ROM, any other optical data storage medium, anyphysical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus 302. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infrared data communications.

In an embodiment, various forms of media are involved in carrying one ormore sequences of one or more instructions to the processor 304 forexecution. For example, the instructions are initially carried on amagnetic disk or solid-state drive of a remote computer. The remotecomputer loads the instructions into its dynamic memory and send theinstructions over a telephone line using a modem. A modem local to thecomputer system 300 receives the data on the telephone line and use aninfrared transmitter to convert the data to an infrared signal. Aninfrared detector receives the data carried in the infrared signal andappropriate circuitry places the data on the bus 302. The bus 302carries the data to the main memory 306, from which processor 304retrieves and executes the instructions. The instructions received bythe main memory 306 may optionally be stored on the storage device 310either before or after execution by processor 304.

The computer system 300 also includes a communication interface 318coupled to the bus 302. The communication interface 318 provides atwo-way data communication coupling to a network link 320 that isconnected to a local network 322. For example, the communicationinterface 318 is an integrated service digital network (ISDN) card,cable modem, satellite modem, or a modem to provide a data communicationconnection to a corresponding type of telephone line. As anotherexample, the communication interface 318 is a local area network (LAN)card to provide a data communication connection to a compatible LAN. Insome implementations, wireless links are also implemented. In any suchimplementation, the communication interface 318 sends and receiveselectrical, electromagnetic, or optical signals that carry digital datastreams representing various types of information.

The network link 320 typically provides data communication through oneor more networks to other data devices. For example, the network link320 provides a connection through the local network 322 to a hostcomputer 324 or to a cloud data center or equipment operated by anInternet Service Provider (ISP) 326. The ISP 326 in turn provides datacommunication services through the world-wide packet data communicationnetwork now commonly referred to as the “Internet” 328. The localnetwork 322 and Internet 328 both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on the network link 320 and through thecommunication interface 318, which carry the digital data to and fromthe computer system 300, are example forms of transmission media. In anembodiment, the network 320 contains the cloud 202 or a part of thecloud 202 described above.

The computer system 300 sends messages and receives data, includingprogram code, through the network(s), the network link 320, and thecommunication interface 318. In an embodiment, the computer system 300receives code for processing. The received code is executed by theprocessor 304 as it is received, and/or stored in storage device 310, orother non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG. 4 shows an example architecture 400 for an autonomous vehicle(e.g., the AV 100 shown in FIG. 1). The architecture 400 includes aperception module 402 (sometimes referred to as a perception circuit), aplanning module 404 (sometimes referred to as a planning circuit), acontrol module 406 (sometimes referred to as a control circuit), alocalization module 408 (sometimes referred to as a localizationcircuit), and a database module 410 (sometimes referred to as a databasecircuit). Each module plays a role in the operation of the AV 100.Together, the modules 402, 404, 406, 408, and 410 may be part of the AVsystem 120 shown in FIG. 1. In some embodiments, any of the modules 402,404, 406, 408, and 410 is a combination of computer software (e.g.,executable code stored on a computer-readable medium) and computerhardware (e.g., one or more microprocessors, microcontrollers,application-specific integrated circuits [ASICs]), hardware memorydevices, other types of integrated circuits, other types of computerhardware, or a combination of any or all of these things).

In use, the planning module 404 receives data representing a destination412 and determines data representing a trajectory 414 (sometimesreferred to as a route) that can be traveled by the AV 100 to reach(e.g., arrive at) the destination 412. In order for the planning module404 to determine the data representing the trajectory 414, the planningmodule 404 receives data from the perception module 402, thelocalization module 408, and the database module 410.

The perception module 402 identifies nearby physical objects using oneor more sensors 121, e.g., as also shown in FIG. 1. The objects areclassified (e.g., grouped into types such as pedestrian, bicycle,automobile, traffic sign, etc.) and a scene description including theclassified objects 416 is provided to the planning module 404.

The planning module 404 also receives data representing the AV position418 from the localization module 408. The localization module 408determines the AV position by using data from the sensors 121 and datafrom the database module 410 (e.g., a geographic data) to calculate aposition. For example, the localization module 408 uses data from a GNSS(Global Navigation Satellite System) sensor and geographic data tocalculate a longitude and latitude of the AV. In an embodiment, dataused by the localization module 408 includes high-precision maps of theroadway geometric properties, maps describing road network connectivityproperties, maps describing roadway physical properties (such as trafficspeed, traffic volume, the number of vehicular and cyclist trafficlanes, lane width, lane traffic directions, or lane marker types andlocations, or combinations of them), and maps describing the spatiallocations of road features such as crosswalks, traffic signs or othertravel signals of various types.

The control module 406 receives the data representing the trajectory 414and the data representing the AV position 418 and operates the controlfunctions 420 a-c (e.g., steering, throttling, braking, ignition) of theAV in a manner that will cause the AV 100 to travel the trajectory 414to the destination 412. For example, if the trajectory 414 includes aleft turn, the control module 406 will operate the control functions 420a-c in a manner such that the steering angle of the steering functionwill cause the AV 100 to turn left and the throttling and braking willcause the AV 100 to pause and wait for passing pedestrians or vehiclesbefore the turn is made.

Autonomous Vehicle Inputs

FIG. 5 shows an example of inputs 502 a-d (e.g., sensors 121 shown inFIG. 1) and outputs 504 a-d (e.g., sensor data) that is used by theperception module 402 (FIG. 4). One input 502 a is a LiDAR (LightDetection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1). LiDARis a technology that uses light (e.g., bursts of light such as infraredlight) to obtain data about physical objects in its line of sight. ALiDAR system produces LiDAR data as output 504 a. For example, LiDARdata is collections of 3D or 2D points (also known as a point clouds)that are used to construct a representation of the environment 190.

Another input 502 b is a RADAR system. RADAR is a technology that usesradio waves to obtain data about nearby physical objects. RADARs canobtain data about objects not within the line of sight of a LiDARsystem. A RADAR system 502 b produces RADAR data as output 504 b. Forexample, RADAR data are one or more radio frequency electromagneticsignals that are used to construct a representation of the environment190.

Another input 502 c is a camera system. A camera system uses one or morecameras (e.g., digital cameras using a light sensor such as acharge-coupled device [CCD]) to obtain information about nearby physicalobjects. A camera system produces camera data as output 504 c. Cameradata often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). In some examples, the camerasystem has multiple independent cameras, e.g., for the purpose ofstereopsis (stereo vision), which enables the camera system to perceivedepth. Although the objects perceived by the camera system are describedhere as “nearby,” this is relative to the AV. In use, the camera systemmay be configured to “see” objects far, e.g., up to a kilometer or moreahead of the AV. Accordingly, the camera system may have features suchas sensors and lenses that are optimized for perceiving objects that arefar away.

Another input 502 d is a traffic light detection (TLD) system. A TLDsystem uses one or more cameras to obtain information about trafficlights, street signs, and other physical objects that provide visualnavigation information. A TLD system produces TLD data as output 504 d.TLD data often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). A TLD system differs from a systemincorporating a camera in that a TLD system uses a camera with a widefield of view (e.g., using a wide-angle lens or a fish-eye lens) inorder to obtain information about as many physical objects providingvisual navigation information as possible, so that the AV 100 has accessto all relevant navigation information provided by these objects. Forexample, the viewing angle of the TLD system may be about 120 degrees ormore.

In some embodiments, outputs 504 a-d are combined using a sensor fusiontechnique. Thus, either the individual outputs 504 a-d are provided toother systems of the AV 100 (e.g., provided to a planning module 404 asshown in FIG. 4), or the combined output can be provided to the othersystems, either in the form of a single combined output or multiplecombined outputs of the same type (e.g., using the same combinationtechnique or combining the same outputs or both) or different types type(e.g., using different respective combination techniques or combiningdifferent respective outputs or both). In some embodiments, an earlyfusion technique is used. An early fusion technique is characterized bycombining outputs before one or more data processing steps are appliedto the combined output. In some embodiments, a late fusion technique isused. A late fusion technique is characterized by combining outputsafter one or more data processing steps are applied to the individualoutputs.

FIG. 6 shows an example of a LiDAR system 602 (e.g., the input 502 ashown in FIG. 5). The LiDAR system 602 emits light 604 a-c from a lightemitter 606 (e.g., a laser transmitter). Light emitted by a LiDAR systemis typically not in the visible spectrum; for example, infrared light isoften used. Some of the light 604 b emitted encounters a physical object608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Lightemitted from a LiDAR system typically does not penetrate physicalobjects, e.g., physical objects in solid form.) The LiDAR system 602also has one or more light detectors 610, which detect the reflectedlight. In an embodiment, one or more data processing systems associatedwith the LiDAR system generates an image 612 representing the field ofview 614 of the LiDAR system. The image 612 includes information thatrepresents the boundaries 616 of a physical object 608. In this way, theimage 612 is used to determine the boundaries 616 of one or morephysical objects near an AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown inthis figure, the AV 100 receives both camera system output 504 c in theform of an image 702 and LiDAR system output 504 a in the form of LiDARdata points 704. In use, the data processing systems of the AV 100compares the image 702 to the data points 704. In particular, a physicalobject 706 identified in the image 702 is also identified among the datapoints 704. In this way, the AV 100 perceives the boundaries of thephysical object based on the contour and density of the data points 704.

FIG. 8 shows the operation of the LiDAR system 602 in additional detail.As described above, the AV 100 detects the boundary of a physical objectbased on characteristics of the data points detected by the LiDAR system602. As shown in FIG. 8, a flat object, such as the ground 802, willreflect light 804 a-d emitted from a LiDAR system 602 in a consistentmanner. Put another way, because the LiDAR system 602 emits light usingconsistent spacing, the ground 802 will reflect light back to the LiDARsystem 602 with the same consistent spacing. As the AV 100 travels overthe ground 802, the LiDAR system 602 will continue to detect lightreflected by the next valid ground point 806 if nothing is obstructingthe road. However, if an object 808 obstructs the road, light 804 e-femitted by the LiDAR system 602 will be reflected from points 810 a-b ina manner inconsistent with the expected consistent manner. From thisinformation, the AV 100 can determine that the object 808 is present.

Path Planning

FIG. 9 shows a block diagram 900 of the relationships between inputs andoutputs of a planning module 404 (e.g., as shown in FIG. 4). In general,the output of a planning module 404 is a route 902 from a start point904 (e.g., source location or initial location), and an end point 906(e.g., destination or final location). The route 902 is typicallydefined by one or more segments. For example, a segment is a distance tobe traveled over at least a portion of a street, road, highway,driveway, or other physical area appropriate for automobile travel. Insome examples, e.g., if the AV 100 is an off-road capable vehicle suchas a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV, pick-uptruck, or the like, the route 902 includes “off-road” segments such asunpaved paths or open fields.

In addition to the route 902, a planning module also outputs lane-levelroute planning data 908. The lane-level route planning data 908 is usedto traverse segments of the route 902 based on conditions of the segmentat a particular time. For example, if the route 902 includes amulti-lane highway, the lane-level route planning data 908 includestrajectory planning data 910 that the AV 100 can use to choose a laneamong the multiple lanes, e.g., based on whether an exit is approaching,whether one or more of the lanes have other vehicles, or other factorsthat vary over the course of a few minutes or less. Similarly, in someimplementations, the lane-level route planning data 908 includes speedconstraints 912 specific to a segment of the route 902. For example, ifthe segment includes pedestrians or un-expected traffic, the speedconstraints 912 may limit the AV 100 to a travel speed slower than anexpected speed, e.g., a speed based on speed limit data for the segment.

In an embodiment, the inputs to the planning module 404 includesdatabase data 914 (e.g., from the database module 410 shown in FIG. 4),current location data 916 (e.g., the AV position 418 shown in FIG. 4),destination data 918 (e.g., for the destination 412 shown in FIG. 4),and object data 920 (e.g., the classified objects 416 as perceived bythe perception module 402 as shown in FIG. 4). In some embodiments, thedatabase data 914 includes rules used in planning. Rules are specifiedusing a formal language, e.g., using Boolean logic. In any givensituation encountered by the AV 100, at least some of the rules willapply to the situation. A rule applies to a given situation if the rulehas conditions that are met based on information available to the AV100, e.g., information about the surrounding environment. Rules can havepriority. For example, a rule that says, “if the road is a freeway, moveto the leftmost lane” can have a lower priority than “if the exit isapproaching within a mile, move to the rightmost lane.”

FIG. 10 shows a directed graph 1000 used in path planning, e.g., by theplanning module 404 (FIG. 4). In general, a directed graph 1000 like theone shown in FIG. 10 is used to determine a path between any start point1002 and end point 1004. In real-world terms, the distance separatingthe start point 1002 and end point 1004 may be relatively large (e.g, intwo different metropolitan areas) or may be relatively small (e.g., twointersections abutting a city block or two lanes of a multi-lane road).

In an embodiment, the directed graph 1000 has nodes 1006 a-drepresenting different locations between the start point 1002 and theend point 1004 that could be occupied by an AV 100. In some examples,e.g., when the start point 1002 and end point 1004 represent differentmetropolitan areas, the nodes 1006 a-d represent segments of roads. Insome examples, e.g., when the start point 1002 and the end point 1004represent different locations on the same road, the nodes 1006 a-drepresent different positions on that road. In this way, the directedgraph 1000 includes information at varying levels of granularity. In anembodiment, a directed graph having high granularity is also a subgraphof another directed graph having a larger scale. For example, a directedgraph in which the start point 1002 and the end point 1004 are far away(e.g., many miles apart) has most of its information at a lowgranularity and is based on stored data, but also includes some highgranularity information for the portion of the graph that representsphysical locations in the field of view of the AV 100.

The nodes 1006 a-d are distinct from objects 1008 a-b which cannotoverlap with a node. In an embodiment, when granularity is low, theobjects 1008 a-b represent regions that cannot be traversed byautomobile, e.g., areas that have no streets or roads. When granularityis high, the objects 1008 a-b represent physical objects in the field ofview of the AV 100, e.g., other automobiles, pedestrians, or otherentities with which the AV 100 cannot share physical space. In anembodiment, some or all of the objects 1008 a-b are a static objects(e.g., an object that does not change position such as a street lamp orutility pole) or dynamic objects (e.g., an object that is capable ofchanging position such as a pedestrian or other car).

The nodes 1006 a-d are connected by edges 1010 a-c. If two nodes 1006a-b are connected by an edge 1010 a, it is possible for an AV 100 totravel between one node 1006 a and the other node 1006 b, e.g., withouthaving to travel to an intermediate node before arriving at the othernode 1006 b. (When we refer to an AV 100 traveling between nodes, wemean that the AV 100 travels between the two physical positionsrepresented by the respective nodes.) The edges 1010 a-c are oftenbidirectional, in the sense that an AV 100 travels from a first node toa second node, or from the second node to the first node. In anembodiment, edges 1010 a-c are unidirectional, in the sense that an AV100 can travel from a first node to a second node, however the AV 100cannot travel from the second node to the first node. Edges 1010 a-c areunidirectional when they represent, for example, one-way streets,individual lanes of a street, road, or highway, or other features thatcan only be traversed in one direction due to legal or physicalconstraints.

In an embodiment, the planning module 404 uses the directed graph 1000to identify a path 1012 made up of nodes and edges between the startpoint 1002 and end point 1004.

An edge 1010 a-c has an associated cost 1014 a-b. The cost 1014 a-b is avalue that represents the resources that will be expended if the AV 100chooses that edge. A typical resource is time. For example, if one edge1010 a represents a physical distance that is twice that as another edge1010 b, then the associated cost 1014 a of the first edge 1010 a may betwice the associated cost 1014 b of the second edge 1010 b. Otherfactors that affect time include expected traffic, number ofintersections, speed limit, etc. Another typical resource is fueleconomy. Two edges 1010 a-b may represent the same physical distance,but one edge 1010 a may require more fuel than another edge 1010 b,e.g., because of road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the startpoint 1002 and end point 1004, the planning module 404 typically choosesa path optimized for cost, e.g., the path that has the least total costwhen the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG. 11 shows a block diagram 1100 of the inputs and outputs of acontrol module 406 (e.g., as shown in FIG. 4). A control module operatesin accordance with a controller 1102 which includes, for example, one ormore processors (e.g., one or more computer processors such asmicroprocessors or microcontrollers or both) similar to processor 304,short-term and/or long-term data storage (e.g., memory random-accessmemory or flash memory or both) similar to main memory 306, ROM 308, andstorage device 310, and instructions stored in memory that carry outoperations of the controller 1102 when the instructions are executed(e.g., by the one or more processors).

In an embodiment, the controller 1102 receives data representing adesired output 1104. The desired output 1104 typically includes avelocity, e.g., a speed and a heading. The desired output 1104 can bebased on, for example, data received from a planning module 404 (e.g.,as shown in FIG. 4). In accordance with the desired output 1104, thecontroller 1102 produces data usable as a throttle input 1106 and asteering input 1108. The throttle input 1106 represents the magnitude inwhich to engage the throttle (e.g., acceleration control) of an AV 100,e.g., by engaging the steering pedal, or engaging another throttlecontrol, to achieve the desired output 1104. In some examples, thethrottle input 1106 also includes data usable to engage the brake (e.g.,deceleration control) of the AV 100. The steering input 1108 representsa steering angle, e.g., the angle at which the steering control (e.g.,steering wheel, steering angle actuator, or other functionality forcontrolling steering angle) of the AV should be positioned to achievethe desired output 1104.

In an embodiment, the controller 1102 receives feedback that is used inadjusting the inputs provided to the throttle and steering. For example,if the AV 100 encounters a disturbance 1110, such as a hill, themeasured speed 1112 of the AV 100 is lowered below the desired outputspeed. In an embodiment, any measured output 1114 is provided to thecontroller 1102 so that the necessary adjustments are performed, e.g.,based on the differential 1113 between the measured speed and desiredoutput. The measured output 1114 includes measured position 1116,measured velocity 1118, (including speed and heading), measuredacceleration 1120, and other outputs measurable by sensors of the AV100.

In an embodiment, information about the disturbance 1110 is detected inadvance, e.g., by a sensor such as a camera or LiDAR sensor, andprovided to a predictive feedback module 1122. The predictive feedbackmodule 1122 then provides information to the controller 1102 that thecontroller 1102 can use to adjust accordingly. For example, if thesensors of the AV 100 detect (“see”) a hill, this information can beused by the controller 1102 to prepare to engage the throttle at theappropriate time to avoid significant deceleration.

FIG. 12 shows a block diagram 1200 of the inputs, outputs, andcomponents of the controller 1102. The controller 1102 has a speedprofiler 1202 which affects the operation of a throttle/brake controller1204. For example, the speed profiler 1202 instructs the throttle/brakecontroller 1204 to engage acceleration or engage deceleration using thethrottle/brake 1206 depending on, e.g., feedback received by thecontroller 1102 and processed by the speed profiler 1202.

The controller 1102 also has a lateral tracking controller 1208 whichaffects the operation of a steering controller 1210. For example, thelateral tracking controller 1208 instructs the steering controller 1210to adjust the position of the steering angle actuator 1212 depending on,e.g., feedback received by the controller 1102 and processed by thelateral tracking controller 1208.

The controller 1102 receives several inputs used to determine how tocontrol the throttle/brake 1206 and steering angle actuator 1212. Aplanning module 404 provides information used by the controller 1102,for example, to choose a heading when the AV 100 begins operation and todetermine which road segment to traverse when the AV 100 reaches anintersection. A localization module 408 provides information to thecontroller 1102 describing the current location of the AV 100, forexample, so that the controller 1102 can determine if the AV 100 is at alocation expected based on the manner in which the throttle/brake 1206and steering angle actuator 1212 are being controlled. In an embodiment,the controller 1102 receives information from other inputs 1214, e.g.,information received from databases, computer networks, etc.

Planning with Dynamic State

FIGS. 13A-13B show graphs of velocity squared versus positionillustrating how soft and hard constraints affect a projected route andmaximum acceleration/deceleration profiles associated with AV 100 inaccordance with some embodiments. In particular, FIG. 13A shows how amaximum velocity associated with particular segments of projected route1302 is bounded by soft constraints 1304. Soft constraints 1304 canresult from many different road conditions. For example, constraint1304-1 can correspond with a maximum recommended speed for a particularcurve, constraint 1304-2 can correspond with a reduced speed limitassociated with a construction zone and constraint 1304-3 can correspondwith a vehicle reducing speed to accommodate a rougher or more slipperyroad surface. In each case, a velocity achieved by AV 100 is reducedover the course of a particular stretch of road for a number ofdifferent reasons.

FIG. 13B show maximum acceleration profile 1352 and maximum decelerationprofile 1354 achievable by AV 100 in some embodiments. Maximumacceleration profile 1352 is affected by hard constraints 1356. Hardconstraint 1356-1 can correspond with an acceleration limit designed toprevent damage to a powertrain of AV 100. Hard constraint 1356-2 cancorrespond with a maximum acceleration limit that prevents tipping giventhe curvature of a road across which AV 100 is traversing and hardconstraint 1356-3 can correspond to AV 100 approaching its maximumspeed. It should be noted that a user taking manual control of AV 100might be able to exceed the max deceleration profile or drive past oneof hard constraints 1356, but that hard constraints 1356 represent ahard limit for what the autonomous driving system is able to performwithout user intervention or the engagement of a system such as acollision avoidance system.

Constraints can also be characterized as static and dynamic. A staticconstraint being one considered unlikely to change during the passage ofAV 100 and a dynamic constrain being one considered likely to relocateor change in state during the passage of AV 100. As will be described ingreater detail below, processors associated with the autonomous drivingsystem can generally be configured to determine static constraintsbefore dynamic constraints so that the system knows what options areavailable for handling objects likely to change position or state whendetermining the dynamic constraints that ameliorate risks associatedwith less predictable objects.

FIG. 14A shows a top down view of AV 100 traversing a street 1402 as atraffic light at traffic light junction 1404 turns yellow. Following themaximum kinematic profile depicted in FIG. 14B, AV 100 can get throughintersection 1404 prior to the traffic light turning red (e.g., based ontypical yellow light timings) without exceeding hard constraint 1(depicted in FIG. 14B). Hard constraint 1 can represent a maximum speedlimit for street 1402. In this scenario, there is a construction zone1406 on the left side of the road (relative to the direction of travelof AV 100). Hard constraint 2 (depicted in FIG. 14B) represents aconstraint AV 100 must follow when passing by construction zone 1406that prevents AV 100 from exceeding what has been determined to be amaximum safe speed in close proximity to construction zone 1406. AV 100can be configured to determine a maximum safe speed in a number ofdifferent ways. In some embodiments, a default maximum speed limit forconstruction zones on city streets is used. In some embodiments, postedconstruction zone speed limits are used in lieu of the default maximumspeed limit when available. In some embodiments, AV 100 can beconfigured to determine a maximum safe speed limit dynamically based onone or more factors such as proximity to construction zone 1406 atclosest approach, other vehicular traffic and activity at constructionzone 1406.

In the scenario shown in FIG. 14A, when following the max accelerationprofile shown in FIG. 14B, AV 100 cannot accelerate up to the maximumspeed limit represented by hard constraint 1 until after AV 100 hasentered intersection 1404 due to hard constraint 2. Hard constraint 2 isconsidered to be a static constraint since the cones associated withconstruction zone 1406 are unlikely to go away prior to AV 100 passingthrough intersection 1404. For this reason, AV 100 is able to determinethat it cannot safely get through intersection 1404 before the trafficlight turns red without violating hard constraint 2. In this scenario,AV 100 can apply a dynamic constraint represented by the updated profileshown in FIG. 14B, which allows AV 100 to stop before enteringintersection 1404 without violating any system constraints. The updatedconstraint is characterized as a dynamic constraint because AV 100 isnot entirely sure what the duration of the yellow light will be. FIG.14B also depicts a typical profile with less aggressive acceleration anda more conservative speed limit for construction zone 1406 depicted asthe soft constraint line. Clearly, AV 100 is also unable to enterintersection 1404 prior to the traffic light turning red. It should beappreciated that in some scenarios, choosing the maximum accelerationprofile or even following the typical profile will allow AV 100 to clearan intersection without running a red light or having a collision. AV100 will generally choose the profile that optimizes safety and comfort.

FIGS. 15A-15C show a scenario in which a pedestrian 1504 (e.g., ajaywalker) crosses street 1502. In FIG. 15A, AV 100 detects pedestrian1504 beginning to cross street 1502. Upon detecting pedestrian 1504entering street 1502, AV 100 determines that at the current speed of AV100, which for purposes of this example is the maximum posted speedlimit for this street, and based on a predicted behavior of pedestrian1504, pedestrian 1504 could be positioned in either of the two lanes ofstreet 1502. The potential locations for pedestrian 1504 at a predictedtime at which AV 100 has its closest approach to pedestrian 1504 isdepicted in FIG. 15A as temporal zone 1506 and is based on predictedfuture movement of pedestrian 1504. In some embodiments, the likelihoodof the object being within an identified temporal zone is at a level ofabove 99%. In this scenario, AV 100 can determine the size and shape oftemporal zone 1506 to be relatively large (e.g., several times the sizeof pedestrian 1504; compared to temporal zone 1506 in FIGS. 15B and 15Cdescribed below) since a historical database accessible by AV 100indicates pedestrians often travel unpredictably when not in acrosswalk, e.g., instead of travelling directly across the street aswould be expected in a crosswalk scenario. In some embodiments, thehistorical database is stored onboard AV 100, while in other embodimentsat least a portion of the database is stored outside of AV 100. In thecurrent scenario, due to the close proximity of temporal zone 1506 to AV100, AV 100 determines it is unable to fully stop prior to intersectingtemporal zone 1506. In this situation, AV 100 will move to the left lane(e.g., the upper lane in FIG. 15A) because AV 100 is able to determinefrom temporal zone 1506 that pedestrian 1504 is less likely to occupythe left lane than the right lane (e.g., the lower lane in FIG. 15A) dueto the relative areas of temporal zone 1506 occupying the right and leftlanes.

FIG. 15B shows that after AV 100 changes lanes, temporal zone 1506 stilloccupies a portion of the left lane. FIG. 15B also depicts that temporalzone 1506 shrinks (e.g., is reduced in size and/or changes shape) as AV100 approaches pedestrian 1504 on account of AV 100 being more confidentabout the position 1506 that pedestrian 1504 will occupy at the time ofclosest approach between AV 100 and pedestrian 1504. Confidence levelscan increase for a number of reasons including at least the following:(1) there is less time for pedestrian 1504 to move as AV 100 approachespedestrian 1504, thereby reducing the possible distance that can becovered by pedestrian 1504; and (2) AV 100 has had time to measure aspeed and direction in which pedestrian 1504 is travelling, therebyallowing AV 100 to more accurately predict the behavior (i.e. directionof travel) of pedestrian 1504.

In the case that the maximum posted speed limit is considered by AV 100to be a soft constraint, AV 100 can proceed to accelerate above themaximum posted speed limit to reduce the amount of time pedestrian 1504has to reach the left lane. In some embodiments, AV 100 is authorized toaccelerate to a speed a threshold amount above the posted maximum speedlimit when doing so is performed as part of a collision avoidancemaneuver. FIG. 15C shows how by accelerating above the posted maximumspeed limit, temporal zone 1506 is further reduced in size, and ispushed back sufficiently into the right lane for AV 100 to avoidpedestrian 1504 with a high degree of certainty. This example shows howAV 100 is able to bypass the posted speed limit, when considered to be asoft constraint, in order to avoid the possibility of striking thepedestrian (e.g., avoiding striking the pedestrian is a higher prioritythan obeying the posted speed limit). While AV 100 will generallyattempt to follow all constraints, this shows how in certaincircumstances AV 100 will prioritize following a hard dynamic constraint(pedestrian) over a soft static constraint (posted speed limit). Itshould be noted that in cases where pedestrian 1504 is a pedestriancrossing street 1502 at a crosswalk, an initial shape of temporal zone1506 can be more defined by this type of pavement marking, therebyallowing AV 100 to make a more refined initial projection of size andshape of the pedestrian (e.g., an initial projection that does notchange significantly over time (e.g., less than 50%, less than 25%, lessthan 10%)). An object determined to be a pedestrian using an authorizedmeans of crossing street 1502 can also be assigned a differentclassification. For example, a pedestrian in a crosswalk might bedetermined to have a more predictable speed than a pedestrian that isnot in a crosswalk, resulting in the temporal zone for the pedestrianbeing smaller and easier for AV 100 to accommodate.

While FIGS. 15A-15C show only a single dynamic object being tracked andavoided, in some embodiments, AV 100 can track multiple dynamic and/orstatic objects (e.g., concurrently), such that one or more of theobjects (e.g., each detected and/or tracked object) acts as a constraintlimiting possible actions that can be taken by AV 100 without risking acollision. Because the behavior of the detected objects can be moreaccurately predicted given the historical data correlation, AV 100 ismore likely to be able to take actions that help it avoid collisions indifficult situations.

FIG. 16A shows a scenario in which AV 100 is approaching intersection1602 on road 1604 and determines based on the behavior of vehicle 1606and its current projected route shown in FIG. 16B that vehicle 1606 islikely to enter intersection 1602 at the same time as AV 100. AV 100also determines that it is too close to intersection 1602 to stop intime to avoid entering intersection 1602, as shown by the maximumdeceleration profile line depicted in FIG. 16B. In some embodiments, theslope of the maximum deceleration profile line can depend upon afollowing distance of a vehicle trailing the autonomous vehicle. Theslope in this case may correspond to the maximum deceleration theautonomous vehicle determines that it can make without causing thetrailing vehicle to impact the autonomous vehicle. In response to therenot being enough room to stop the autonomous vehicle, AV 100 determinesthat the most effective way to avoid a collision with vehicle 1606 is toaccelerate as it approaches intersection 1602 and maintain a higher thanprojected speed through intersection 1602. In order to get AV 100 toperform this action, an additional constraint can be introduced to AV100 that requires AV 100 to meet a minimum speed constraint designed toallow AV 100 to get through intersection 1602 prior to vehicle 1606 witha determined amount of certainty. FIG. 16B depicts the minimum speedconstraint, which dictates a higher velocity than the projected routebut a lower velocity than the max acceleration profile. This allows AV100 to follow a relatively narrow window that can keep it safe fromcollision with vehicle 1606. In some embodiments, AV 100 can beconfigured to instead adopt the maximum acceleration profile depicted inFIG. 16B to maximize the amount of space between AV 100 and vehicle1606.

Example Process for Detecting Objects and Operating the Vehicle Based onthe Detection of the Objects

FIG. 17 is a flow chart of an example process 1700 for detecting objectsin an environment and operating an autonomous vehicle (e.g., AV 100)based on the detection of objects (e.g., 1406, 1504). For convenience,the process 1700 will be described as being performed by a system of oneor more computers located in one or more locations. For example, thecomputing system 300 of FIG. 3, appropriately programmed in accordancewith this specification, can perform the process 1700. In particular, at1702 one or more sensors of an autonomous vehicle detect a plurality ofobjects proximate the autonomous vehicle's projected route. In someembodiments, sensors on nearby cars or mounted in nearby intersectionscan also be used to detect the presence of unexpected objects proximatethe projected route. Sensor data generated by the sensors and associatedwith the plurality of objects is obtained by at least one processor ofthe autonomous vehicle. At 1704, the at least one processor determinesstatic constraints that limit a trajectory of the autonomous vehicle.The static constraints prevent the autonomous vehicle from becomingsubject to any non-temporal risks associated with a first subset of theplurality of objects. Non-temporal risks are generally risks that theprocessor determines are unlikely to change as the autonomous vehicletraverses the projected route.

At 1706, the at least one processor predicts a position and speed of theautonomous vehicle as a function of time along the projected route. Insome embodiments, the predicted position and speed can be based on themaximum speed allowable by the static constraints. For example, in thecase the only static constraint is a posted speed limit. The predictioncould be based on the autonomous vehicle traveling at the posted speedlimit. In some embodiment, the posted speed limit can be a soft staticconstraint as the processor can be allowed to set a trajectory of theautonomous vehicle that exceeds the posted speed limit in a case wheredoing so could be performed to avoid a collision or other undesirableevent.

At 1708 the at least one processor is configured to identify andcharacterize temporal risks posed by a second subset of the plurality ofobjects. Characterization of the temporal risks can be based onhistorical data that helps the at least one processor predict futuremovement and/or change of state of the second subset of objects. Forexample, the historical data can include recordings showing how long atraffic signal takes to change from yellow to red. A confidence in theprediction can cause the at least one processor to be more or lessconservative in making decisions about changes to the trajectory. Forexample, the processor might be less confident about the walking speedof a pedestrian than it is in the duration of a yellow light.Consequently, any dynamic constraints directed toward avoidance of thepedestrian would accommodate a larger range of walking speeds for thepedestrian.

At 1710, the at least one processor determines dynamic constraints basedon the predicted position and speed of the autonomous vehicle.Performing the dynamic constraint calculation after determining thestatic constraints helps narrow down the options the autonomous vehiclehas for avoiding less predictable detected objects that pose a temporalrisk to the autonomous vehicle. For example, static constraints could besuch that the autonomous vehicle isn't able to accelerate sufficientlyto avoid a temporal risk and must instead decelerate to avoid damage ordanger posed by the temporal risk.

At 1712 the at least one processor adjusts the trajectory of theautonomous vehicle in accordance with the static constraints and thedynamic constraints. It should be noted that the at least one processormay make changes to the dynamic constraints that result in additionaladjustments to the trajectory. For example, a pedestrian could suddenlydecide to run across a crosswalk or suddenly stop in the middle of acrosswalk. In such a case, the dynamic constraints would be updatedresulting in a change to the trajectory to accommodate the updateddynamic constraint. At 1714 the at least one processor navigates theautonomous vehicle in accordance with the latest update to thetrajectory.

In the foregoing description, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The description and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction. Any definitions expressly set forthherein for terms contained in such claims shall govern the meaning ofsuch terms as used in the claims. In addition, when we use the term“further comprising,” in the foregoing description or following claims,what follows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

1. A system, comprising: at least one processor at least one memorystoring instructions thereon that, when executed by the at least oneprocessor, cause the at least one processor to perform operationscomprising: obtaining sensor data generated by a sensor of an autonomousvehicle, the sensor data being associated with a plurality of objectsproximate a projected route of the autonomous vehicle; determiningstatic constraints that limit a trajectory of the autonomous vehiclealong the projected route based on non-temporal risks associated with afirst subset of the plurality of objects; predicting a position andspeed of the autonomous vehicle as a function of time along theprojected route based on the static constraints; identifying temporalrisks associated with a second subset of the plurality of objects basedon the predicted position and speed of the autonomous vehicle;determining dynamic constraints that further limit the trajectory of theautonomous vehicle along the projected route to help the autonomousvehicle avoid the temporal risks associated with the second subset ofthe plurality of objects; and adjusting the trajectory of the autonomousvehicle in accordance with the static constraints and the dynamicconstraints; and navigating the autonomous vehicle in accordance withthe adjusted trajectory.
 2. The system of claim 1, wherein identifyingtemporal risks comprises identifying temporal zones along the projectedroute for the autonomous vehicle to avoid based on predicted behavior ofthe second subset of the plurality of objects.
 3. The system of claim 2,wherein an object from the second subset of the plurality of objects isa pedestrian in a crosswalk.
 4. The system of claim 3, wherein theoperations further comprise setting a size of the temporal zoneassociated with the pedestrian based at least in part on a confidence inthe predicted behavior of the pedestrian.
 5. The system of claim 3,wherein the operations further comprise adjusting the trajectory afterthe temporal zone is detected to be a threshold distance away from theprojected route.
 6. The system of claim 3, wherein a size of thetemporal zone is affected by a size of the crosswalk.
 7. The system ofclaim 2, wherein an object from the second subset of the plurality ofobjects is a traffic intersection and a temporal zone associated withthe traffic intersection extends across a majority of the trafficintersection.
 8. The system of claim 7, wherein the operations furthercomprise in response to a traffic light associated with the trafficintersection turning yellow, determining a dynamic constraintestablishing a minimum speed profile allowing the autonomous vehicle toenter the traffic intersection prior to the traffic light turning red ordetermine a dynamic constraint establishing a maximum speed profileallowing the autonomous vehicle to stop safely prior to entering thetraffic intersection.
 9. The system of claim 8, wherein determining thedynamic constraint comprises predicting a time at which the trafficlight will turn red based on historical data that includes an averageamount of time for the traffic light to turn from yellow to red.
 10. Thesystem of claim 1, wherein predicting a position and speed of theautonomous vehicle as a function of time based on the static constraintscomprises determining a maximum possible speed profile of the vehiclebased on the static constraints.
 11. The system of claim 1, wherein oneof the static constraints is a posted speed limit.
 12. The system ofclaim 1, wherein the plurality of objects includes objects trailing theautonomous vehicle and wherein the dynamic constraints include a maximumdeceleration limit based upon one of the object trailing the autonomousvehicle at less than a threshold distance.
 13. The system of claim 1,wherein the operations further comprise: determining a type of eachobject captured by the sensor of the autonomous vehicle; andprioritizing avoidance of a first risk associated with a first type ofobject over avoidance of a second risk associated with a second type ofobject.
 14. The system of claim 1, wherein the operations furthercomprise updating one or more of the dynamic constraints in response toa detected change in an object of the second subset of the plurality ofobjects.
 15. The system of claim 1, wherein an object from the secondsubset of the plurality of objects is a pedestrian crossing a streetoutside of a crosswalk.
 16. The system of claim 1, wherein theoperations further comprise activating an emergency collision avoidancesystem in response to a determination that sensor data from the sensorindicates an imminent collision.
 17. The system of claim 1, wherein thestatic constraints comprise soft constraints and hard constraints,wherein a dynamic constraints can cause the autonomous vehicle toviolate a soft static constraint but not a hard static constraint. 18.The autonomous vehicle of claim 17, wherein dynamic constraints onlyviolate soft static constraints when required to avoid temporal risks.19. A non-transitory computer-readable storage medium comprisinginstructions stored thereon that, when executed by at least oneprocessor, cause the at least one processor to carry out operationscomprising: obtaining sensor data generated by a sensor of an autonomousvehicle, the sensor data being associated with a plurality of objectsproximate a projected route of the autonomous vehicle; determiningstatic constraints that limit a trajectory of the autonomous vehiclealong the projected route based on non-temporal risks associated with afirst subset of the plurality of objects; predicting a position andspeed of the autonomous vehicle as a function of time along theprojected route based on the static constraints; identifying temporalrisks associated with a second subset of the plurality of objects basedon the predicted position and speed of the autonomous vehicle;determining dynamic constraints that further limit the trajectory of theautonomous vehicle along the projected route to help the autonomousvehicle avoid the temporal risks associated with the second subset ofthe plurality of objects; and adjusting the trajectory of the autonomousvehicle in accordance with the static constraints and the dynamicconstraints; and navigating the autonomous vehicle in accordance withthe adjusted trajectory.
 20. A method performed by an autonomous vehiclefollowing a projected route, the method comprising: obtaining sensordata generated by a sensor of the autonomous vehicle, the sensor databeing associated with a plurality of objects proximate the projectedroute of the autonomous vehicle; determining static constraints thatlimit a trajectory of the autonomous vehicle along the projected routebased on non-temporal risks associated with a first subset of theplurality of objects; predicting a position and speed of the autonomousvehicle as a function of time along the projected route based on thestatic constraints; identifying temporal risks associated with a secondsubset of the plurality of objects based on the predicted position andspeed of the autonomous vehicle; determining dynamic constraints thatfurther limit the trajectory of the autonomous vehicle along theprojected route to help the autonomous vehicle avoid the temporal risksassociated with the second subset of the plurality of objects; adjustingthe trajectory of the autonomous vehicle in accordance with the staticconstraints and the dynamic constraints; and navigating the autonomousvehicle in accordance with the adjusted trajectory.